





'''simple docstring'''







class 							__SCREAMING_SNAKE_CASE    :






				"""simple docstring"""







				def __init__(    self	:   Optional[int]   ):
								_a				      =					""
								_a				      =					""
								_a				      =					[]




				def     UpperCamelCase__  (    self	:   Tuple   ,							__a	:   List[str]   ,							__a	:   Tuple   ):
								if m == -1:
												return n + 1
								elif n == -1:
												return m + 1
								elif self.dp[m][n] > -1:
												return self.dp[m][n]
								else:
												if self.worda[m] == self.worda[n]:
																_a				      =					self.__min_dist_top_down_dp(m - 1   ,							n - 1   )
												else:
																_a				      =					self.__min_dist_top_down_dp(__a   ,							n - 1   )
																_a				      =					self.__min_dist_top_down_dp(m - 1   ,							__a   )
																_a				      =					self.__min_dist_top_down_dp(m - 1   ,							n - 1   )
																_a				      =					1 + min(__a   ,							__a   ,							__a   )

												return self.dp[m][n]




				def     UpperCamelCase__  (    self	:   Optional[int]   ,							__a	:   Optional[int]   ,							__a	:   Union[str, Any]   ):
								_a				      =					worda
								_a				      =					worda
								_a				      =					[[-1 for _ in range(len(__a   )   )] for _ in range(len(__a   )   )]

								return self.__min_dist_top_down_dp(len(__a   ) - 1   ,							len(__a   ) - 1   )




				def     UpperCamelCase__  (    self	:   List[str]   ,							__a	:   List[Any]   ,							__a	:   Optional[Any]   ):
								_a				      =					worda
								_a				      =					worda
								_a				      =					len(__a   )
								_a				      =					len(__a   )
								_a				      =					[[0 for _ in range(n + 1   )] for _ in range(m + 1   )]

								for i in range(m + 1   ):
												for j in range(n + 1   ):
																if i == 0:  # first string is empty
																				_a				      =					j
																elif j == 0:  # second string is empty
																				_a				      =					i
																elif worda[i - 1] == worda[j - 1]:  # last characters are equal
																				_a				      =					self.dp[i - 1][j - 1]
																else:
																				_a				      =					self.dp[i][j - 1]
																				_a				      =					self.dp[i - 1][j]
																				_a				      =					self.dp[i - 1][j - 1]
																				_a				      =					1 + min(__a   ,							__a   ,							__a   )
								return self.dp[m][n]


if __name__ == "__main__":
					lowerCAmelCase_   :       Any       =     EditDistance()

					print('****************** Testing Edit Distance DP Algorithm ******************')
					print()

					lowerCAmelCase_   :       Tuple       =     input('Enter the first string: ').strip()
					lowerCAmelCase_   :       Dict       =     input('Enter the second string: ').strip()

					print()
					print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
					print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
					print()
					print('*************** End of Testing Edit Distance DP Algorithm ***************')







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






